Predictive Analytics & Modeling Using Spss

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Published 10/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.69 GB | Duration: 12h 35m
Predictive Analytics & Modeling course aims to enhance predictive modelling skills across business sectors

What you'll learn
It aims to provide and enhance predictive modelling skills across business sectors/domains
Quantitative methods and predictive modelling concepts could be extensively used in understanding the current customer behavior
The course picks theoretical and practical datasets for predictive analysis
Observations, interpretations, predictions and conclusions are explained then and there on the examples as we proceed through the training
Requirements
Prior knowledge of Quantitative Methods, MS Office and Paint will be useful.
Description
Predictive modelling course aims to provide and enhance predictive modelling skills across business sectors/domains. Quantitative methods and predictive modelling concepts could be extensively used in understanding the current customer behavior, financial markets movements, and studying tests and effects in medicine and in pharma sectors after drugs are administered. The course picks theoretical and practical datasets for predictive analysis. Implementations are done using SPSS software. Observations, interpretations, predictions and conclusions are explained then and there on the examples as we proceed through the training. The course also emphasizes on the higher order regression models such as quadratic and polynomial regressions which aren't covered in other online courses Essential skillsets - Prior knowledge of Quantitative methods and MS Office, Paint Desired skillsets -- Understanding of Data Analysis and VBA toolpack in MS Excel will be usefulThe course works across multiple software packages such as SPSS, MS Office, PDF writers, and Paint. This course is to specifically learn about Descriptive Statistics, Means, Standard Deviation and T-test Understanding Means, Standard Deviation, Skewness, Kurtosis and T-test concepts• Interpretation of descriptive statistics and t- values• Implementation on example/sample datasets using SPSSThis course is not focused on specific set of sectors and domains because it can used by professionals across sectors. However, the list of professionals bulleted below should be able to make the best use of itStudentsQuantitative and Predictive Modellers and ProfessionalsCFA's and Equity Research professionalsPharma and research scientists
Overview
Section 1: Importing Dataset
Lecture 1 Importing Datasets in Text and CSV
Lecture 2 Importing Datasets xlsx and xls Formats
Lecture 3 Importing Datasets xlsx and xls Formats Continue
Lecture 4 Understanding User Operating Concepts
Lecture 5 Software Menus
Lecture 6 Understanding Mean Standard Deviation
Lecture 7 Other Concepts of Understanding Mean SD
Lecture 8 Implementation Using SPSS
Lecture 9 Implementation using SPSS Continues
Section 2: Correlation Techniques
Lecture 10 Basic Correlation Theory
Lecture 11 Implementation
Lecture 12 Data Editor
Lecture 13 Simple Scatter Plot
Lecture 14 Heart Pulse
Lecture 15 Statistics Viewer
Lecture 16 Heart Pulse (Before and After RUN)
Lecture 17 Interpretation and Implementation on Datasets Example 1
Lecture 18 Interpretation and Implementation on Datasets Example 2
Lecture 19 Interpretation and Implementation on Datasets Example 3
Lecture 20 Interpretation and Implementation on Datasets Example 4
Section 3: Linear Regression Modeling
Lecture 21 Introduction to Linear Regression Modeling Using SPSS
Lecture 22 Linear Regression
Lecture 23 Stock Return
Lecture 24 T-Value
Lecture 25 Scatter Plot Rril vs Rbse
Lecture 26 Create Attributes for Variables
Lecture 27 Scatter Plot Rify vs Rbse
Lecture 28 Regression Equation
Lecture 29 Interpretation
Lecture 30 Copper Expansion
Lecture 31 Copper Expansion Example
Lecture 32 Copper Expansion Example Continue
Lecture 33 Energy Consumption
Lecture 34 Observations
Lecture 35 Energy Consumption Example
Lecture 36 Debt Assessment
Lecture 37 Debt Assessment Continue
Lecture 38 Debt to Income Ratio
Lecture 39 Credit Card Debt
Lecture 40 Predicted values Using MS Excel
Lecture 41 Predicted values Using MS Excel Continue
Section 4: Multiple Regression Modeling
Lecture 42 Introduction to Basic Multiple Regression
Lecture 43 Important Output Variables
Lecture 44 Multiple Regression Example Part 1
Lecture 45 Multiple Regression Example Part 2
Lecture 46 Multiple Regression Example Part 3
Lecture 47 Multiple Regression Example Part 4
Lecture 48 Multiple Regression Example Part 5
Lecture 49 Multiple Regression Example Part 6
Lecture 50 Multiple Regression Example Part 7
Lecture 51 Multiple Regression Example Part 8
Lecture 52 Multiple Regression Example Part 9
Lecture 53 Multiple Regression Example Part 10
Lecture 54 Multiple Regression Example Part 11
Lecture 55 Multiple Regression Example Part 12
Lecture 56 Multiple Regression Example Part 13
Lecture 57 Multiple Regression Example Part 14
Section 5: Logistic Regression
Lecture 58 Understanding Logistic Regression Concepts
Lecture 59 Working on IBM SPSS Statistics Data Editor
Lecture 60 SPSS Statistics Data Editor Continues
Lecture 61 IBM SPSS Viewer
Lecture 62 Variable in the Equation
Lecture 63 Implementation Using MS Excel
Lecture 64 Smoke Preferences
Lecture 65 Heart Pulse Study
Lecture 66 Heart Pulse Study Continues
Lecture 67 Variables in the Equation
Lecture 68 Smoking Gender Equation
Lecture 69 Generating Output and Observations
Lecture 70 Generating Output and Observations Continues
Lecture 71 Interpretation of Output Example
Section 6: Multinomial Regression
Lecture 72 Introduction to Multinomial-Polynomial Regression
Lecture 73 Example 1 Health Study of Marathoners
Lecture 74 Note
Lecture 75 Case Processing Summary
Lecture 76 Model Fitting Information
Lecture 77 Asymptotic Correlation Matrix
Lecture 78 Understanding Dataset
Lecture 79 Generating Output
Lecture 80 Parameters Estimates
Lecture 81 Asymptotic Correlations Metrics
Lecture 82 Interpretation of Output
Lecture 83 Interpretation of Output Continues
Lecture 84 Interpretation of Estimates
Lecture 85 Understand Interpretation
Students, Quantitative and Predictive Modellers and Professionals, CFA's and Equity Research professionals, Pharma and research scientists

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